Quantized genetic resampling particle filtering for vision-based ground moving target tracking

This paper concentrates on the quantized measurements fusion problem. Because of the non-Gaussian property of quantized measurements, classical nonlinear filters become not applicable, especially when the quantization noise is large. Motivated by the above problem, we propose a novel particle-based filtering named quantized genetic resampling particle filtering (QGRPF) in order to fuse quantized measurements optimally and improve estimation accuracy. Unlike the Gaussian assumption in traditional filers, the probability density function (PDF) of sensor measurement is modeled here as a discrete PDF that consists of a series of Dirac impulses. The particle filtering is employed to estimate the state, where the posterior of the state based on quantized measurements is approximated and updated by a set of weighted particles. As the likelihood function is a multi-dimensional integral of Gaussian density, which has no analytical solution, Genz's transformation and quasi-Monte Carlo method are combined to calculate the integral numerically. By integrating genetic resampling method into the particle filtering method, the proposed method can avoid particle degeneracy, in the meantime, guarantee particle diversity. The proposed QGRPF is demonstrated with an illustrative vision-based ground moving target (GMT) tracking example. Simulation results show the proposed approach is effective in fusing quantized measurements.